Technology and Health Care最新文献

Background: Heart disease represents the leading cause of death globally. Timely diagnosis and treatment can prevent cardiovascular issues. An Electrocardiograms (ECG) serves as a diagnostic tool for identifying heart difficulties. Cardiovascular Disease (CVD) often gets identified through ECGs. Deep learning (DL) garners attention in healthcare due to its potential in swiftly diagnosing ECG anomalies, crucial for patient monitoring. Conversely, automatic CVD detection from ECGs poses a challenging task, wherein rule-based diagnostic models usually achieve top-notch performance. These models encounter complications in supervision vast volumes of diverse data, demanding widespread analysis and medical capability to ensure precise CVD diagnosis.

Objective: This study aims to enhance cardiovascular disease diagnosis by combining symptom-based detection and ECG analysis.

Methods: To enhance these experiments, we built a novel automated prediction method based on a Feed Forward Neural Network (FFNN) model. The fundamental objective of our method is to develop the accuracy of ECG diagnosis. Our strategy employs chaos theory and destruction analysis to combine optimum deep learning features with a well-organized set of ECG properties. In addition, we use the constant-Q non-stationary Gabor transform (CQNGT) to convert one-dimensional ECG data into a two-dimensional picture. A pre-trained FFNN processes this image. To identify significant features from the FFNN output that correspond with the ECG data, we employ pairwise feature proximity.

Results: According to experimental findings, the suggested system, FFNN-CQNGT, surpasses other state-of-the-art systems in terms of precision of 94.89%, computational efficiency of 2.114 ms, accuracy of 95.55%, specificity of 93.77%, and sensitivity of 93.99% and MSE 40.32%.

Conclusion: Contributing an automated ECG-based DL system based on FFNN-CQNGT for early-stage cardiovascular disease identification and classification holds great potential for both patient care and public health.

Background: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning.

Objective: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet's deep residual learning to address training issues in deep neural networks. At the same time, U-Net's structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training.

Methods: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation.

Results: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation.

Conclusion: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.

Background: Endoscopic submucosal dissection (ESD) is a well-established treatment for gastrointestinal tumors and enables en bloc resection. Adequate counter traction with good visualization is important for safe and effective dissection.

Objective: Based on magnetic anchor-guided endoscopic submucosal dissection (MAG-ESD), we would like to explore the feasibility of magnetic hydrogel as an internal magnetic anchor that can be injected into the submucosa through an endoscopic needle to assist colonic endoscopic submucosal dissection.

Methods: This prospective trial was conducted on 20 porcine colons ex vivo. We injected magnetic hydrogel into submucosa of the porcine colons ex vivo for MAG-ESD to evaluate the traction effect and operation satisfaction.

Results: Magnetic hydrogel assisted ESD was successfully performed on 20 porcine colons ex vivo. Adequate counter traction with good visualization was successfully obtained during the procedure of dissection.

Conclusion: Magnetic hydrogel assisted MAG-ESD is feasible and effective.